Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent. - Rishav Chourasia; Jiayuan Ye 001; Reza Shokri (NeurIPS, 2021)

Enhanced Membership Inference Attacks against Machine Learning Models. - Jiayuan Ye 001; Aadyaa Maddi; Sasi Kumar Murakonda; Reza Shokri (CoRR, 2021)

Enhanced Membership Inference Attacks against Machine Learning Models. - Jiayuan Ye 001; Aadyaa Maddi; Sasi Kumar Murakonda; Vincent Bindschaedler; Reza Shokri (CCS, 2022)

Differentially Private Learning Needs Hidden State (Or Much Faster Convergence). - Jiayuan Ye 001; Reza Shokri (NeurIPS, 2022)

Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. - Zebang Shen; Jiayuan Ye 001; Anmin Kang; Hamed Hassani; Reza Shokri (ICLR, 2023)

Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks. - Jiayuan Ye 001; Zhenyu Zhu; Fanghui Liu 001; Reza Shokri; Volkan Cevher (NeurIPS, 2023)

Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy. - Chendi Wang; Buxin Su; Jiayuan Ye 001; Reza Shokri; Weijie J. Su (NeurIPS, 2023)

Privacy Analyses in Machine Learning. - Jiayuan Ye 001 (CCS, 2024)

Leave-one-out Distinguishability in Machine Learning. - Jiayuan Ye 001; Anastasia Borovykh; Soufiane Hayou; Reza Shokri (ICLR, 2024)

How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning. - Yao Tong; Jiayuan Ye 001; Sajjad Zarifzadeh; Reza Shokri (ICLR, 2025)

Instance-Optimality for Private KL Distribution Estimation. - Jiayuan Ye 001; Vitaly Feldman; Kunal Talwar (CoRR, 2025)